DiffSoup: Direct Differentiable Rasterization of Triangle Soup for Extreme Radiance Field Simplification
Radiance field reconstruction aims to recover high-quality 3D representations from multi-view RGB images. Recent advances, such as 3D Gaussian splatting, enable real-time rendering with high visual fidelity on sufficiently powerful graphics hardware. However, efficient online transmission and rendering across diverse platforms requires drastic model simplification, reducing the number of primitives by several orders of magnitude. We introduce DiffSoup, a radiance field representation that employs a soup (i.e., a highly unstructured set) of a small number of triangles with neural textures and binary opacity. We show that this binary opacity representation is directly differentiable via sto...
| Authors | Kenji Tojo, Bernd Bickel, Nobuyuki Umetani |
|---|---|
| Primary category | cs.GR |
| Published | 28 March 2026 |
| Last updated at source | 15 August 2026 |
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